BackgroundRisk factors for hip fracture are well studied because of the negative impact on patients and the community, with mortality in the first year being almost 30% in the elderly. Age, gender and fall risk-increasing drugs, identified by the National Board of Health and Welfare in Sweden, are well known risk factors for hip fracture, but how multimorbidity level affects the risk of hip fracture during use of fall risk-increasing drugs is to our knowledge not as well studied. This study explored the relationship between use of fall risk-increasing drugs in combination with multimorbidity level and risk of hip fracture in an elderly population.MethodsData were from Östergötland County, Sweden, and comprised the total population in the county aged 75 years and older during 2006. The odds ratio (OR) for hip fracture during use of fall risk-increasing drugs was calculated by multivariate logistic regression, adjusted for age, gender and individual multimorbidity level. Multimorbidity level was estimated with the Johns Hopkins ACG Case-Mix System and grouped into six Resource Utilization Bands (RUBs 0–5).Results2.07% of the study population (N = 38,407) had a hip fracture during 2007. Patients using opioids (OR 1.56, 95% CI 1.34-1.82), dopaminergic agents (OR 1.78, 95% CI 1.24-2.55), anxiolytics (OR 1.31, 95% CI 1.11-1.54), antidepressants (OR 1.66, 95% CI 1.42-1.95) or hypnotics/sedatives (OR 1.31, 95% CI 1.13-1.52) had increased ORs for hip fracture after adjustment for age, gender and multimorbidity level. Vasodilators used in cardiac diseases, antihypertensive agents, diuretics, beta-blocking agents, calcium channel blockers and renin-angiotensin system inhibitors were not associated with an increased OR for hip fracture after adjustment for age, gender and multimorbidity level.ConclusionsUse of fall risk-increasing drugs such as opioids, dopaminergic agents, anxiolytics, antidepressants and hypnotics/sedatives increases the risk of hip fracture after adjustment for age, gender and multimorbidity level. Fall risk-increasing drugs, high age, female gender and multimorbidity level, can be used to identify high-risk patients who could benefit from a medication review to reduce the risk of hip fracture.

Objective. To study the associations between active choice of primary care provider and healthcare utilization, multimorbidity, age, and sex, comparing data from primary care and all healthcare in a Swedish population. Design. Descriptive cross-sectional study using descriptive analyses including t-test, correlations, and logistic regression modelling in four separate models. Setting and subjects. The population (151 731) and all healthcare in Blekinge in 2007. Main outcome measure. Actively or passively listed in primary care, registered on 31 December 2007. Results. Number of consultations (OR 1.31, 95% CI 1.30–1.32), multimorbidity level (OR 1.69, 95% CI 1.67–1.70), age (OR 1.03, 95% CI 1.03–1.03), and sex (OR for men 0.67, 95% CI 0.65–0.68) were all associated with registered active listing in primary care. Active listing was more strongly associated with number of consultations and multimorbidity level using primary care data (OR 2.11, 95% CI 2.08–2.15 and OR 2.14, 95% CI 2.11–2.17, respectively) than using data from all healthcare. Number of consultations and multimorbidity level were correlated and had similar associations with active listing in primary care. Modelling number of consultations, multimorbidity level, age, and sex gave four separate models with about 70% explanatory power for active listing in primary care. Combining number of consultations and multimorbidity did not improve the models. Conclusions. Number of consultations and multimorbidity level were associated with active listing in primary care. These factors were also associated with each other differently in primary care than in all healthcare. More complex models including non-health-related individual characteristics and healthcare-related factors are needed to increase explanatory power.

BackgroundHealthcare systems are complex networks where relationships affect outcomes. The importance of primary care increases while health care acknowledges multimorbidity, the impact of combinations of different diseases in one person. Active listing and consultations in primary care could be used as proxies of the relationships between patients and primary care. Our objective was to study hospitalisation as an outcome of primary care, exploring the associations with active listing, number of consultations in primary care and two groups of practices, while taking socioeconomic status and morbidity burden into account.MethodsA cross-sectional study using zero-inflated negative binomial regression to estimate odds of any hospital admission and mean number of days hospitalised for the population over 15 years (N = 123,168) in the Swedish county of Blekinge during 2007. Explanatory factors were listed as active or passive in primary care, number of consultations in primary care and primary care practices grouped according to ownership. The models were adjusted for sex, age, disposable income, education level and multimorbidity level.ResultsMean days hospitalised was 0.94 (95%CI 0.90–0.99) for actively listed and 1.32 (95%CI 1.24–1.40) for passively listed. For patients with 0–1 consultation in primary care mean days hospitalised was 1.21 (95%CI 1.13–1.29) compared to 0.77 (95%CI 0.66–0.87) days for patients with 6–7 consultations. Mean days hospitalised was 1.22 (95%CI 1.16–1.28) for listed in private primary care and 0.98 (95%CI 0.94–1.01) for listed in public primary care, with odds for hospital admission 0.51 (95%CI 0.39–0.63) for public primary care compared to private primary care.ConclusionsActive listing and more consultations in primary care are both associated with reduced mean days hospitalised, when adjusting for socioeconomic status and multimorbidity level.Different odds of any hospitalisation give a difference in mean days hospitalised associated with type of primary care practice.To promote well performing primary care to maintain good relationships with patients could reduce mean days hospitalised.

BackgroundSocioeconomic status and geographical factors are associated with health and use of healthcare. Well-performing primary care contributes to better health and more adequate healthcare. In a primary care system based on patient’s choice of practice, this choice (listing) is a key to understand the system.ObjectiveTo explore the relationship between population and practices in a primary care system based on listing.MethodsCross-sectional population-based study. Logistic regressions of the associations between active listing in primary care, income, education, distances to healthcare and geographical location, adjusting for multimorbidity, age, sex and type of primary care practice.Setting and subjectsPopulation over 15 years (n=123 168) in a Swedish county, Blekinge (151 731 inhabitants), in year 2007, actively or passively listed in primary care. The proportion of actively listed was 68%.Main outcome measureActively listed in primary care on 31 December 2007.ResultsHighest ORs for active listing in the model including all factors according to income had quartile two and three with OR 0.70 (95% CI 0.69 to 0.70), and those according to education less than 9 years of education had OR 0.70 (95% CI 0.68 to 0.70). Best odds for geographical factors in the same model had municipality C with OR 0.85 (95% CI 0.85 to 0.86) for active listing. Akaike’s Information Criterion (AIC) was 124 801 for a model including municipality, multimorbidity, age, sex and type of practice and including all factors gave AIC 123 934.ConclusionsHigher income, shorter education, shorter distance to primary care or longer distance to hospital is associated with active listing in primary care.Multimorbidity, age, geographical location and type of primary care practice are more important to active listing in primary care than socioeconomic status and distance to healthcare.

Objective: Patient-provider relationships with primary care and need for hospitalisations are related within the complex networks comprising healthcare. Our objective was to analyse mean days hospitalised, using registration status (active or passive listing) with a provider and number of consultations as proxies of patient-provider relationships with primary care, adjusting for morbidity burden, age and sex while analysing the contribution of psychiatric disorders. The Johns Hopkins Adjusted Clinical Groups Case-Mix System was used to classify morbidity burden into Resource Utilization Band (RUB) 0-5. Design: Cross-sectional population study using zero-inflated negative binomial regression. Setting and subjects: All population in the Swedish County of Blekinge ( N = 151 731) in 2007. Main outcome measure: Mean days hospitalised. Results: Actively listed were in mean hospitalised for 0.86 (95%CI 0.81–0.92) and passively listed for 1.23 (95%CI 1.09–1.37) days. For 0–1 consultation mean days hospitalised was 1.16 (95%CI 1.08–1.23) and for 4–5 consultations 0.68 (95%CI 0.62–0.75) days. At RUB3, actively listed were in mean hospitalised for 3.45 (95%CI 2.84–4.07) days if diagnosed with any psychiatric disorder and 1.64 (95%CI 1.50–1.77) days if not. Passively listed at RUB3 were in mean hospitalised for 5.17 (95%CI 4.36–5.98) days if diagnosed with any psychiatric disorder and 2.41 (95%CI 2.22–2.60) days if not. Conclusions: Active listing and more consultations were associated with a decrease in mean days hospitalised, especially for patients with psychiatric diagnoses. Implications: Promoting good relationships with primary care could be an opportunity to decrease mean days hospitalised, especially for patients with more complex diagnostic patterns. Key Points Primary care performance, patient-provider relationships and need for hospitalisation are related within the complex networks comprising healthcare systems. Good patient-provider relationships, i.e. more consultations and active listing, with primary care are associated with decreasing mean days hospitalised. The impact of patient-provider relationships in primary care on mean days hospitalised increased when psychiatric disorders added to patient complexity.